13 citations · 49 across the 33 of their papers we have counts for
36 papers
Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents
Ram Rachum, Yotam Amitai, Bálint Gyevnár +2
This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functionally-grounded metrics like…
Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?
Gabriele La Malfa, Nitay Alon, Emanuele La Malfa +2
Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI techniques are increasingly…
HumAIN: Human-Aware Implicit Social Robot Navigation
Daeun Song, Nhat Le, Jeffrey Chen +7
Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation. We present Human-Aware Implicit Soc…
BXRL: Behavior-Explainable Reinforcement Learning
Ram Rachum, Yotam Amitai, Yonatan Nakar +2
A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learnin…
The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processes
Benedikt Hornig, Reuth Mirsky
In shared autonomy, a critical tension arises when an automated assistant must choose between obeying a human's instruction and deliberately overriding it to prevent harm. This saf…
Proceedings of the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind
Nitay Alon, Joseph M. Barnby, Reuth Mirsky +1
This volume includes a selection of papers presented at the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2026 in Singapore on 26th January…
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord acc…
GRAIL: Goal Recognition Alignment through Imitation Learning
Osher Elhadad, Felipe Meneguzzi, Reuth Mirsky
Understanding an agent's goals from its behavior is fundamental to aligning AI systems with human intentions. Existing goal recognition methods typically rely on an optimal goal-or…
Break Out the Silverware -- Semantic Understanding of Stored Household Items
Michaela Levi-Richter, Reuth Mirsky, Oren Glickman
``Bring me a plate.'' For domestic service robots, this simple command reveals a complex challenge: inferring where everyday items are stored, often out of sight in drawers, cabine…
Online Dynamic Goal Recognition in Gym Environments
Shamir Matan, Elhadad Osher, Nageris Ben +1
Goal Recognition (GR) is the task of inferring an agent's intended goal from partial observations of its behavior, typically in an online and one-shot setting. Despite recent advan…
Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates
Reuth Mirsky
Artificial intelligence has made remarkable strides in recent years, achieving superhuman performance across a wide range of tasks. Yet despite these advances, most cooperative AI…
GRAML: Goal Recognition As Metric Learning
Matan Shamir, Reuth Mirsky
Goal Recognition (GR) is the problem of recognizing an agent's objectives based on observed actions. Recent data-driven approaches for GR alleviate the need for costly, manually cr…
Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance
Yotam Amitai, Reuth Mirsky, Ofra Amir
In human-agent teams, openly sharing goals is often assumed to enhance planning, collaboration, and effectiveness. However, direct communication of these goals is not always feasib…
General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning
Osher Elhadad, Owen Morrissey, Reuth Mirsky
Understanding an agent's goal through its behavior is a common AI problem called Goal Recognition (GR). This task becomes particularly challenging in dynamic environments where goa…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming
Myke C. Cohen, David A. Grimm, Reuth Mirsky +1
Animal-Human-Machine (AHM) teams are a type of hybrid intelligence system wherein interactions between a human, AI-enabled machine, and animal members can result in unique capabili…
All You Need is Sally-Anne: ToM in AI Strongly Supported After Surpassing Tests for 3-Year-Olds
Nitay Alon, Joseph Barnby, Reuth Mirsky +1
Theory of Mind (ToM) is a hallmark of human cognition, allowing individuals to reason about others' beliefs and intentions. Engineers behind recent advances in Artificial Intellige…
Goal Recognition using Actor-Critic Optimization
Ben Nageris, Felipe Meneguzzi, Reuth Mirsky
Goal Recognition aims to infer an agent's goal from a sequence of observations. Existing approaches often rely on manually engineered domains and discrete representations. Deep Rec…
To What Extent Does the Perceived Obesity Level of Humanoid Robots Affect People's Trust in Them?
Yoav Yoscovich, Amir Schreiber, Nir Hadar +1
Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field of HRI. While in research regard…
Shared Control with Black Box Agents using Oracle Queries
Inbal Avraham, Reuth Mirsky
Shared control problems involve a robot learning to collaborate with a human. When learning a shared control policy, short communication between the agents can often significantly…
ODGR: Online Dynamic Goal Recognition
Matan Shamir, Osher Elhadad, Matthew E. Taylor +1
Traditionally, Reinforcement Learning (RL) problems are aimed at optimization of the behavior of an agent. This paper proposes a novel take on RL, which is used to learn the policy…
Emergent Dominance Hierarchies in Reinforcement Learning Agents
Ram Rachum, Yonatan Nakar, Bill Tomlinson +2
Modern Reinforcement Learning (RL) algorithms are able to outperform humans in a wide variety of tasks. Multi-agent reinforcement learning (MARL) settings present additional challe…
ICRA Roboethics Challenge 2023: Intelligent Disobedience in an Elderly Care Home
Sveta Paster, Kantwon Rogers, Gordon Briggs +2
With the projected surge in the elderly population, service robots offer a promising avenue to enhance their well-being in elderly care homes. Such robots will encounter complex sc…
Exploring the Cost of Interruptions in Human-Robot Teaming
Swathi Mannem, William Macke, Peter Stone +1
Productive and efficient human-robot teaming is a highly desirable ability in service robots, yet there is a fundamental trade-off that a robot needs to consider in such tasks. On…